clinical judgment in diagnostic errors: let's think about ......with cases that are complex or...

Clinical Judgment in Diagnostic Errors: Let’s Think About Thinking

Laura M. Cascella, MA

If you’ve ever heard someone use the phrase, “it was a judgment call,” you probably assumed

that the person made a decision based on the best available information. That is, the person

assessed the situation, considered relevant data, and came to a conclusion based on factual

information and his/her own opinion, knowledge, and experience.

The term “clinical judgment” refers to a similar process that healthcare providers use to

assess and diagnose patients. A provider’s clinical judgment is informed by information

gathered from the patient,

observation, and the provider’s

own personal experience,

knowledge, practice, and critical-

thinking skills.1

Because clinical judgment is a

complex process that involves various cognitive functions, it’s easy to understand why it is a

driving force in diagnostic errors and diagnosis-related malpractice allegations. In fact,

MedPro Group data show that clinical judgment is a contributing factor in 80 percent of

diagnosis-related claims (Figure 1) — a rate more than double that of the next top

contributing factor. The prevalence of clinical judgment issues is almost certainly tied to the

fact that they tend to be less amenable to “simple” fixes than other contributing factors are,

such as system failures.

Because clinical judgment is a complex

process that involves various cognitive

functions, it’s easy to understand why it is

a driving force in diagnostic errors and

diagnosis-related malpractice allegations.”

Clinical Judgment in Diagnostic Errors: Let’s Think About Thinking 2

Figure 1. Top Risk Factors in Diagnosis-Related Malpractice Claims

Source: MedPro Group closed claims data, 2007–2016

This article will (a) take a closer look at the various judgment issues that contribute to

diagnosis-related malpractice claims, (b) examine how cognition shapes clinical reasoning and

decision-making, (c) discuss how cognitive errors in judgment can occur during the diagnostic

process, and (d) explore proposed solutions and risk strategies for managing clinical judgment

issues.

Clinical Judgment in the Context of Diagnosis-Related Malpractice Claims The concept of clinical judgment as a contributing factor in diagnosis-related malpractice

claims can be difficult to grasp because of its enormity. Simply stated, clinical judgment is a

broad category that includes the clinical areas shown in Figure 2.

80%

33%

17% 17% 17%11%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


Figure 2. Top Issues Associated With Clinical Judgment in Diagnosis-Related Malpractice Claims

Source: MedPro Group closed claims data, 2007–2016

Within clinical judgment, patient assessment issues surface as the top concern. Common

examples of patient assessment issues are:

• Failing to order diagnostic testing or delaying diagnostic testing

• Failing to establish a differential diagnosis

• Maintaining a narrow diagnostic focus

• Failing to gather adequate information related to patient history

• Failing to perform an adequate physical exam

• Failing to rule out or act on abnormal findings

• Misinterpreting diagnostic test results

The other top issues in clinical judgment, as noted in Figure 2, are failure or delay in

obtaining a consult/referral, problems related to selection/management of therapy, and

inadequate patient monitoring. This first of these issues is self-explanatory. Issues related to

selection/management of therapy are heavily associated with choosing an appropriate plan of

96%

40%

24%

8%

0% 20% 40% 60% 80% 100%

Patient assessment issues

Failure or delay in obtaining aconsult/referral

Problems related toselection/management of therapy

Inadequate patient monitoring

% of diagnosis-related claims with clinical judgment issue noted


care, including procedural care. This category also includes issues with ordering medications

appropriate for the patient’s condition. Allegations of inadequate patient monitoring mainly

involve monitoring a patient’s response to a treatment plan.

The breakdown of these issues

helps define the ways in which

clinical judgment errors

contribute to malpractice

claims, but it doesn’t explain

why these circumstances happen. What causes these missteps and lapses in judgment?

Understanding the clinical reasoning and decision-making processes can help explain why

clinical judgment so frequently contributes to diagnostic errors.

Clinical Reasoning and Decision-Making The Institute of Medicine’s (IOM’s) influential report Improving Diagnosis in Health Care

explains that “Clinical reasoning occurs within clinicians’ minds . . . and involves judgment

under uncertainty, with a consideration of possible diagnoses that might explain symptoms

and signs, the harms and benefits of diagnostic testing and treatment for each of those

diagnoses, and patient preferences and values.2

Much of the literature focusing on diagnostic errors and clinical reasoning recognizes the dual

decision-making model, which consists of two reasoning systems as the basis for clinicians’

diagnostic process. In System 1, the reasoning process is automatic, intuitive, reflexive, and

nonanalytic. The clinician arranges patient data into a pattern and arrives at a working

diagnosis based on past experience, knowledge, and/or intuition. In System 2, the reasoning

process is analytic, slow, reflective, and deliberate. This type of thinking often is associated

with cases that are complex or novel, and System 2 thinking involves more cognitive workload

and resources.3

System 1 and System 2 are not mutually exclusive, and clinicians tend to use both, depending

on the circumstances. These systems also may occur in tandem and intervene with or override

each other as situations evolve.4

Understanding the clinical reasoning and

decision-making processes can help explain

why clinical judgment so frequently

contributes to diagnostic errors.”


Research suggests that most clinical work involves System 1 reasoning, particularly as

clinicians gain more experience and knowledge — however, both systems of reasoning are

vulnerable to cognitive errors.5

Cognitive Errors Many types of cognitive errors can occur during the diagnostic process. Describing each is

beyond the scope of this article; however, errors in clinical reasoning can arise from several

sources, including knowledge deficits, faulty heuristics, and affective biases or influences.6

Knowledge Deficits

Gaps in knowledge and clinician inexperience might seem like a logical cause of diagnostic

errors. As such, an individual might assume that younger, less experienced healthcare

providers would be at greater risk of diagnostic pitfalls than experienced clinicians.

Sometimes this is true, and it can show how System 2 clinical reasoning is susceptible to

cognitive errors. Even with a slow, analytic thought process, “clinicians with an inadequate

knowledge base may not have the information necessary to make a correct decision.”7

However, inexperience aside, most cognitive errors are not related to knowledge deficits;

rather, they are the result of errors in data collection, data integration, and data verification,

with “data” referring to clinical information obtained during the provider–patient encounter.8

Further, many diagnostic errors are associated with common diseases and conditions,

suggesting that other problems with clinical reasoning — such as faulty heuristics, cognitive

biases, and affective influences — are the likely culprit (as opposed to an inadequate

knowledge base).

Faulty Heuristics and Cognitive Biases

The term “heuristics” refers to mental shortcuts in the thought process that help conserve

time and effort. These shortcuts are an essential part of thinking, but they also are prone to

errors. Cognitive biases occur when heuristics lead to faulty decision-making.9 Some common

biases included anchoring, availability, and overconfidence.


Anchoring Anchoring refers to a tendency to “anchor” to, or rely too much on, a particular piece of

information — often the initial information obtained, the first symptom, or the first lab

abnormality. Anchoring is closely related to several other biases, including:

• Under-adjustment, which is the inability to revise a diagnosis based on additional

clinical data

• Premature closure, which is the termination of the data-gathering process (e.g.,

patient history, family history, and medication list) before all of the information is

known

• Primacy effect, which is the tendency to show bias toward primary or initial

information

• Confirmation bias, which is the tendency to focus on information that confirms an

initial diagnosis or to manipulate information to fit preconceptions

Availability Availability bias can occur if a clinician considers a diagnosis more likely because it is

forefront in his/her mind. Past experience and recent, frequent, or prominent cases can all

play a role in availability bias.

For example, a clinician who has recently diagnosed an elderly patient with dementia might

be more likely to make the same diagnosis in another elderly patient who has signs of

confusion and memory loss — when, in fact, the patient’s symptoms might be indicative of

another problem, such as vitamin B12 deficiency.

Overconfidence This bias can occur if a clinician overestimates his/her own knowledge and ability, which can

prevent the clinician from gathering and assessing ample information. Overconfidence might

result from a lack of feedback related to diagnostic accuracy, which in turn may lead to an

overestimation of diagnostic precision. To this point, overconfidence might increase as a

clinician’s level of expertise and experience increases.10


Context Effect Context effect can occur if a clinician misinterprets information or a situation based on the

context in which it is presented. For example, if a patient presents with chest pain and has a

known family history of heart disease, a clinician might interpret the pain as a likely symptom

of a heart attack, when in fact the cause is a broken rib.

Affective Biases

Whereas cognitive biases are lapses in thinking, the terms “affective biases” and “affective

influences” refer to emotions and feelings that can sway clinical reasoning and decision-

making.11 For example, preconceived notions and stereotypes about a patient might influence

how a healthcare provider views the patient’s complaints and symptoms. If the patient has a

history of substance abuse, for instance, the provider might view complaints about pain as

drug-seeking behavior. Although this impulse might be accurate, the patient could potentially

have a legitimate clinical issue.

Additionally, negative feelings about a

patient can cause a provider to

consciously or subconsciously blame the

patient for his/her symptoms or

condition — a bias called attribution error. For example, a patient’s obesity might be

attributed to laziness or general disregard for health and wellness. Similarly, a patient who

does not adhere to his/her care plan might be viewed as difficult — in reality, though, the

nonadherence might be related to financial issues or health literacy barriers.

Attribution error also is common in elderly patients because clinicians have a tendency to

attribute these patients’ symptoms to advancing age or chronic complaining, rather than

exploring other potential causes.12

Positive feelings about patients also can affect diagnostic decisions. In outcome bias, for

example, a provider may overlook certain clinical data in order to select a diagnosis with

better outcomes. By doing so, the provider is placing more value on what he/she hopes will

happen, rather than what might realistically happen.

Preconceived notions and stereotypes

about a patient might influence how

a healthcare provider views the

patient’s complaints and symptoms.”


In addition to positive and negative feelings about patients, clinician and patient

characteristics — such as age, gender, socio-economic status, and ethnicity — also can affect

the diagnostic process. Consider, for example, that research has shown that women are less

likely than men to be treated aggressively for

pain and must wait longer to receive treatment

for acute pain.13

Beyond negative and positive feelings and

stereotypes, a variety of other affective biases

also can influence a clinician’s reasoning.

Examples include:

• Environmental circumstances, e.g., high

levels of noise or frequent interruptions

• Sleep deprivation, irritability, fatigue,

and stress

• Mood disorders, mood variations, and

anxiety disorders14

The complex interaction between these

cognitive and affective biases can have a profound effect on clinical reasoning and decision-

making, which in turn can lead to various lapses in clinical judgment. The following case

studies provide two examples of how cognitive errors can result in diagnostic missteps.

Bias in Pain Management

Research focusing on disparities in pain

management has noted bias as a

contributing factor. Failure to treat pain

or poorly treated pain can interfere with

how patients recover from illnesses and

procedures, which can potentially

cascade into numerous patient safety

and financial consequences, such as

increased morbidity, hospitalizations and

readmissions, and liability exposure.

To learn more about tackling this issue,

read Lurking Beneath the Surface: Bias

in Pain Management.

Case Study 1: Medical

A 34-year-old male presented to his primary care doctor with sternal pain after lifting a

boat in his backyard. The pain increased when the patient raised his arms. An ECG was

ordered, and the results were negative. The patient was not referred for cardiac enzyme

testing because the doctor decided that muscle strain was the cause of the patient’s

symptoms. The doctor cleared the patient to go on vacation. Two days into his vacation,

the patient died from a heart attack.

https://www.medpro.com/documents/10502/2820774/Article_Bias+in+Pain+Management.pdf

https://www.medpro.com/documents/10502/2820774/Article_Bias+in+Pain+Management.pdf


Case Study 1: Medical (continued)

Discussion: This case offers a good example of anchoring bias. Knowing that the patient

had recently lifted a boat, the doctor honed in on muscle strain as the likely cause of the

patient’s pain. The negative results from the ECG reinforced the narrow diagnostic focus.

As a result, the doctor failed to order further testing and prematurely terminated the

data-gathering process.

Further investigation would have likely revealed that the patient was a heavy smoker and

drinker. He also had a family history of cardiovascular disease, and both his father and

grandfather died in their early forties. An affective bias might have been at play in this

case as well; the doctor might have considered a cardiac condition less likely based on the

patient’s young age.

Case Study 2: Dental

A patient who had undergone radiation therapy for cancer of the soft palate presented to

his general dentist for routine care. Because of severe xerostomia, the dentist and patient

were unable to control the patient’s caries.

After multiple attempts to restore the severely compromised teeth, the dentist decided to

remove the remaining mandibular teeth and insert a complete lower denture; however, he

did not suggest any precautionary measures, such as hyperbaric oxygen, prior to the

extractions.

After a series of denture adjustments, the soft tissue on both the right and left

mandibular ridges did not heal, and the patient would periodically remove small pieces of

bone. The patient returned to the general dentist at least seven times to complain about

the discomfort, bone spicules, a foul odor in his mouth, and episodes of swelling.


Proposed Solutions Although cognitive processes are well-studied, further research is needed to determine how

best to prevent the flaws in clinical judgment that can lead to diagnostic errors. A number of

potential solutions have been proposed, including implementing strategies to improve

teamwork, adjusting processes and workflows, using diagnostic aids, and exploring debiasing

techniques.

Case Study 2: Dental (continued)

After approximately 1 year, the general dentist referred the patient to an oral and

maxillofacial surgeon (OMS). The surgeon developed a plan of care for the patient that

included hyperbaric oxygen treatments and removal of the remaining maxillary teeth, as

well as repair of the mandibular defects.

During the course of treatment, the OMS noted that the mandible was fractured. External

fixation and a bone graft were required to stabilize the fracture.

Discussion: A number of clinical judgment lapses complicated this case and ultimately led

to a malpractice lawsuit against the general dentist. The first was the issue of selecting

and managing the patient’s therapy. Prior to removing the mandibular teeth, the dentist

did not recommend a hyperbaric oxygen protocol or other precautionary measures,

despite the patient’s medical history.

Following the procedure, the patient presented on multiple occasions with complaints,

but the dentist failed to identify the underlying issue or recommend treatment. Finally,

the delay in referring the patient to an OMS was alleged to have contributed to the

patient’s poor outcome.

A knowledge deficit also may have contributed to this case, as the dentist had limited

experience with cases of this level of severity. Additionally, overconfidence might have

been a factor in the dentist choosing to manage the case himself instead of providing an

immediate referral.


The IOM’s top recommendation in Improving Diagnosis in Health Care is facilitating better

teamwork to strengthen the diagnostic process. This recommendation includes supporting an

environment that is conducive to collaboration, providing technology that assists with

communication, establishing measurable processes and feedback mechanisms, and engaging

patients and their families in the diagnostic process.

The IOM’s recommendation represents a major conceptual shift because it distributes

diagnostic responsibility across “the diagnostic team” rather than placing responsibility solely

on the treating clinician. As a result, the diagnostic team must have the knowledge, skills,

resources, and competency to support the diagnostic process. To this end, the IOM also

recommends an increased emphasis on clinical reasoning and decision-making in medical

education, including a strong focus on heuristics and biases.

Other studies on diagnostic errors and clinical judgment suggest using evidence-based

decision support systems, clinical guidelines, checklists, and clinical pathways to support the

reasoning and decision-making processes. However, they note that although these tools can

be useful, “unless they are well integrated in the workflow, they tend to be underused.”15

Similarly, health information technology (IT)

shows promise in supporting diagnostic decision-

making and potentially reducing errors; yet,

many experts in healthcare and technology agree

that health IT is still very flawed and has not yet

reached its full potential.16

A variety of debiasing techniques also have been

proposed as a way to address clinical judgment

issues. Examples of these techniques include

situational awareness and metacognition, which

can help healthcare providers think critically

about their own thought processes and how biases might affect them. Cognitive forcing

functions also might be helpful; these strategies are designed to assist clinicians in self-

monitoring decisions and avoiding potential diagnostic pitfalls.17 Other methods, such as

Clinical Reasoning Toolkit

The Society to Improve Diagnosis in

Medicine’s Clinical Reasoning Toolkit

supports awareness and better

understanding of diagnostic reasoning,

cognitive psychology, and diagnostic

errors. The toolkit includes resources

for clinicians, educators, researchers

and patients.

http://www.improvediagnosis.org/page/clinicalreasoning


perspective-taking, emotional regulation, and partnership-building, also can help reduce bias

and promote empathy, positive feelings, and patient-centered care.

Although many of these techniques show promise, more research is needed to evaluate their

efficacy and to determine the feasibility of introducing them into busy clinical environments.

Risk Management Strategies As researchers continue to explore long-term solutions to errors in clinical judgment,

healthcare providers can proactively implement strategies to help mitigate risks associated

with clinical reasoning, cognition, and decision-making. The following list offers suggestions

for managing these risks within various practice settings:

• Update and review patients’ medical histories, problem lists, medication lists, and

allergy information at each visit. Make sure patients’ health records reflect their most

recent information.

• Consider using a checklist or template to guide taking each patient’s medical history

and performing a thorough physical exam. In a busy healthcare environment, these

tools can help ensure consistency in approach and prevent oversights.

• Perform complete patient assessments, including establishing differential diagnoses,

considering appropriate diagnostic testing, and carefully reviewing test results.

• Engage patients and their families in the diagnostic process through education, access

to health records, and opportunities to provide feedback.

• Work with healthcare leaders and providers in the organization to evaluate the benefit

of using clinical pathways to standardize processes and support high-quality care.

Determine how best to implement care pathways into workflow patterns.

• Consider using clinical decision support systems and other technologies, such as

electronic health record alerts, that can support the diagnostic process and improve

collaboration among members of the diagnostic team.


• Incorporate a diagnostic review process into the workflow pattern. The review might

include timeouts to (a) reflect on working diagnoses, (b) seek consultations, and/or

(c) facilitate group decision-making to support clinical reasoning.

• Develop a written policy that outlines how disagreements in diagnosis and care among

the diagnostic team will be managed, including the appropriate chain of command for

escalating conflicts.

• Formalize procedures for over-reads of diagnostic tests and imaging, peer review and

quality improvement, use of diagnostic guidelines, handoffs of patient information both

within and outside the organization, and better access to patients’ records.

• Be aware of common cognitive and affective biases and how they might negatively

affect clinical judgment. Learn about various techniques to address biases, such as

situational awareness, metacognition, perspective-taking, emotional regulation, and

partnership-building.

• Consider group educational opportunities that allow members of the diagnostic team to

explore cognitive biases and develop solutions together.

Take-Away Message Although diagnostic errors have a number of root causes, MedPro claims data show that

clinical judgment is the most common contributing factor. The complex nature of clinical

reasoning and decision-making makes it vulnerable to various cognitive errors, including

knowledge deficits, faulty heuristics, and affective biases. These errors can subconsciously

lead to lapses in judgment, which in turn can cause diagnostic mistakes.

More studies are needed to determine effective approaches for addressing cognitive errors.

However, a number of strategies — such as improving teamwork, increasing cognitive

awareness, and using clinical decision support systems, clinical pathways, checklists, and

debiasing techniques — show promise. By considering how these strategies can be

implemented in everyday clinical activities, healthcare providers can take proactive steps

toward managing diagnostic risks.


Endnotes

1 Kienle, G. S., & Kiene, H. (2011, August). Clinical judgment and the medical profession. Journal of Evaluation

in Clinical Practice, 17(4), 621–627.

2 National Academies of Sciences, Engineering, and Medicine. (2015). Improving diagnosis in health care.

Washington, DC: The National Academies Press.

3 National Academies of Sciences, Engineering, and Medicine, Improving diagnosis in health care; Nendaz, M., &

Perrier, A. (2012, October). Diagnostic errors and flaws in clinical reasoning: Mechanisms and prevention in

practice. Swiss Medical Weekly, 142:w13706; Phua, D. H., & Tan, N. C. (2013). Cognitive aspect of diagnostic

errors. Annals of the Academy of Medicine, Singapore, 42(1), 33–41; Diagnostic errors and flaws in clinical

reasoning: Mechanisms and prevention in practice. Swiss Medical Weekly, 142:w13706; Ely, J. W., Graber, M. L.,

& Crosskerry, P. (2011, March). Checklists to reduce diagnostic errors. Academic Medicine, 86(3), 307–313.

4 National Academies of Science, Engineering, and Medicine, Improving diagnosis in health care.

5 Phua, et al. Cognitive aspect of diagnostic errors; Nendaz, et al., Diagnostic errors and flaws in clinical

reasoning; Crosskerry, P., Singhal, G., & Mamede, S. (2013, October). Cognitive debiasing 1: Origins of bias and

theory of debiasing. BMJ Quality & Safety, 22(Suppl 2), ii58–ii64.

6 Phua, et al. Cognitive aspect of diagnostic errors.


8 Nendaz, et al., Diagnostic errors and flaws in clinical reasoning.

9 Phua, et al. Cognitive aspect of diagnostic errors.

10 Clark, C. (2013, August 27). Physicians’ diagnostic overconfidence may be harming patients. HealthLeaders

Media. Retrieved from www.healthleadersmedia.com/content/QUA-295686/Physicians-Diagnostic-

Overconfidence-May-be-Harming-Patients##; Phua, et al., Cognitive aspect of diagnostic errors.

11 National Academies of Science, Engineering, and Medicine, Improving diagnosis in health care. Crosskerry, P.,

Abbass, A. A., & Wu, A. W. (2008, October). How doctors feel: Affective influences in patient’s safety. Lancet,

372, 1205–1206; Phua, et al. Cognitive aspect of diagnostic errors.

12 Groopman, J. (2008, September/October). Why doctors make mistakes. AARP Magazine, p. 34.

13 Fassler, J. (2015, October 15). How doctors take women’s pain less seriously. The Atlantic. Retrieved from

www.theatlantic.com/health/archive/2015/10/emergency-room-wait-times-sexism/410515/; Hoffman, D. E., &

Tarzian, A. J. (2001). The girl who cried pain: A bias against women in the treatment of pain. Journal of Law,

Medicine, and Ethics, 29, 13-27.

14 Crosskerry, P., et al., How doctors feel.

http://www.healthleadersmedia.com/content/QUA-295686/Physicians-Diagnostic-Overconfidence-May-be-Harming-Patients

http://www.healthleadersmedia.com/content/QUA-295686/Physicians-Diagnostic-Overconfidence-May-be-Harming-Patients

http://www.theatlantic.com/health/archive/2015/10/emergency-room-wait-times-sexism/410515/


15 Ely, et al., Checklists to reduce diagnostic errors.


17 Crosskerry, P. (2003). Cognitive forcing strategies in clinical decisionmaking. Annals of Emergency Medicine,

41, 110–120.

This document should not be construed as medical or legal advice. Because the facts applicable to your

situation may vary, or the laws applicable in your jurisdiction may differ, please contact your attorney or other

professional advisors if you have any questions related to your legal or medical obligations or rights, state or

federal laws, contract interpretation, or other legal questions.

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